Voice Disorder Identification by using Hilbert-Huang Transform (HHT) and K Nearest Neighbor (KNN)

Chen, Lili; Wang, Chaoyu; Chen, Junjiang; Zhang, Xiang; Hu, Xin

doi:10.1016/j.jvoice.2020.03.009

Cited by 36 publications

(30 citation statements)

References 46 publications

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“…Instead, this paper focuses on two publicly available databases: the Saarbruechen Voice Database (SVD) [12][13][14][15][16] and Voice ICar fEDerico II (VOICED) [16][17][18]. The following is a summary of existing approaches applied to the SVD.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The work in [16] also applied VOICED, showing degraded performance in the accuracy, sensitivity, and specificity (of 0.5, 0.458, and 0.643, respectively). Researchers adopted K nearest neighbor (KNN) as a model for the detection of voice disorders [17]. This method achieved an accuracy of 0.933 and outperformed the other algorithms (random forest (0.874) and extra trees (0.863)).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Existing works have applied various algorithms for voice disorder detection [12][13][14][15][16][17][18]. However, further research and exploration are necessary to address the following limitations in existing works.…”

Section: Research Gaps and Motivationmentioning

confidence: 99%

“…Some existing works [12,16,17] did not apply cross-validation during their performance evaluation of the algorithms. The first concern is that one may pick up a biased training dataset to train the detection model, which takes advantages of this bias by yielding high accuracy.…”

Section: Imentioning

confidence: 99%

“…Current works [12][13][14][15][16][17][18] have formulated the voice disorder detection problem as binary detection that only outputs a healthy or pathological result. It is desirable for machine learning algorithms to suggest the actual type of voice disorder to minimize screening time by medical professions.…”

Section: Imentioning

confidence: 99%

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Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset

2020

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The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10%. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9–12.9% and 9.1–44.8%, respectively.

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